What is “Random Forest” in machine learning?
a) An ensemble learning technique that combines multiple decision trees
b) A clustering method for high-dimensional data
c) A dimensionality reduction algorithm
d) A method to split the dataset into training and testing sets
Answer:
a) An ensemble learning technique that combines multiple decision trees
Explanation:
Random Forest is an ensemble learning technique that builds multiple decision trees and combines their outputs to make predictions. Each tree is trained on a random subset of the data and features, which helps reduce the risk of overfitting.
Random Forests are powerful because they combine the predictions of many weak learners (individual decision trees) to form a strong learner. The final prediction is typically the majority vote in classification tasks or the average in regression tasks.
This technique is widely used in both classification and regression problems due to its robustness, accuracy, and ability to handle missing data or outliers effectively.